With the advent of high-throughput molecular assay technologies, biologists are having to deal with the analysis of high-dimensional genomic datasets. While statistical methods have been proposed for issues such as differential expression with these data, relatively little work has been done in terms of incorporating biological knowledge in the statistical analysis of high-throughput biological data in human disease settings. [unreadable] [unreadable] In this grant, we propose the development of statistical procedures for modeling of complex high-dimensional biological data with an emphasis towards incorporating functional biological knowledge. The methods we propose will be implemented and distributed in software available to biologists. While the major biological data example in this grant is from a microarray experiment in cancer, the methods proposed here are general and can be developed for studying high-dimensional genotype-phenotype associations in other contexts. Given this, we propose the following aims: [unreadable] [unreadable] 1. Development of hierarchical models for modelling of high-dimensional data in complex cell systems. [unreadable] 2. Development of statistical methodology for the identification of disease progressor genes. [unreadable] 3. Development of statistical methodology for assessing the role of functional pathways based on integration of gene expression and pathway data. [unreadable] 4. Development of statistical methodology for determining regions of overexpression and underexpression based on integration of gene expression and chromosomal location data. [unreadable] 5. Dissemination of these results in user-friendly statistical software. [unreadable] [unreadable]